The generative dataless methods focus on incorporating the supervision provided by seed words into the generative assumption of corpora. The representatives are mainly based on topic modeling such as latent Dirichlet allocation (LDA) [1]}, which are widely applied in various tasks [2]}, [3]}, [4]}. The ClassifyLDA [5]} and its extension TLC++ [6]} manually make correspondences between labels and temporary topics, and then continue to infer the model using those topics as initialization. Recently, seed-guided topic model (STM) [7]} and Laplacian seed word topic model (LapSWTM) [8]}, incorporate the supervision information by constructing document-specific category priors with seed word occurrences, and further schemes, e.g., manifold regularizer [8]}, are used to spread the supervision.
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